18K stars in a week

jev-ultrafast splits every browser decision into operation, target and value.

starmex

@starmexxx

JEV JUST HIT 18K STARS IN A WEEK AND BOOKS ZURICH TO LONDON ON GOOGLE FLIGHTS IN 7.1 SECONDS WHILE CHATGPT OPERATOR STILL NEEDS 3 MINUTES AT $200 A MONTH jev-ultrafast splits every browser decision into operation, click target, and typed text, so one llm request replaces five and the agent finishes the task before the page finishes animating [here's the setup i'd use:] 1. install it git clone http://github.com/browser-use/jev-ultrafast cd jev-ultrafast uv sync cp .env.example .env 2. add the keys typesafe_api_key → .env openrouter_key → .env (for mercury 2.5) 3. wire the models mercury 2.5 → typing (diffusion, milliseconds not tokens) gemini 3.1 → target selection glm 4.6 → fallback text helper 4. build around stages goal → snapshot → operation → target → execute → verify keep the dom snapshot separate from the click so a stale page never restarts the whole run 5. run it uv run jev localhost:8766 → start demo → run automatically 6. monetize it fiverr → "i book any flight, hotel, or form in 10 seconds" at $30 a task telegram bot → travel search on demand at $5 a query b2b → sell it to travel agencies as "one seat replaces one junior researcher" at $500/mo white label → wrap as a "concierge agent" and charge $20/mo per user same reason browser-use's cloud waitlist filled up in a week: one indexed action space, no screenshots, no restarts. http://github.com/browser-use/jev-ultrafast

Sep 23, 2026 · 20 likesOpen on X

JEV passed 18,000 stars in its first week. starmexxx points at jev-ultrafast, which splits every browser decision into an operation, a click target and a value, and describes it as a browser automation speed record.

Open the source

jev-browser in Cline

idan levin

@0xidanlevin

We just ran Jev on our WebMCP benchmark. The result: basically broke the benchmark. Jev + Mercury 2.5 (a fast, low-cost LLM) using WebMCP solved 100% of the tasks at roughly 112× lower model cost than GPT-6 Astra using computer use with code execution. Compared to Astra using screenshot-based computer use, the model cost was 245× lower (!). We also compared Jev operating the browser with and without WebMCP. We used Browser Use’s open-source Ultrafast, with some improvements to the harness to make it more reliable across the benchmark. Jev’s browser-control accuracy on its own was not amazing - adding WebMCP nearly doubled the number of solved tasks, from 25/49 to 49/49, while reducing model cost by 18% (more on why below). The benchmark and methodology are fully open and reproducible. Full results: https://webmcp.com/benchmark A few words on how the Jev + WebMCP harness works and why this is exciting: Jev receives text as input and a set of discrete options it can choose from. With WebMCP, those options are the tools exposed by the website. At each step, Jev sees the task, the available tools and previous results, then picks what to do next. The limitation is that Jev can’t generate arbitrary text, which you need for tool arguments. For example, it can choose the search_products tool, but it can’t generate the search query itself. So we split the work: Jev picks the tool and Mercury 2.5 generates the arguments if needed. This works well because turns out most of the cognitive load in these tasks is around choosing the right action. The argument generation itself is relatively simple, so we can delegate to a small and very fast model. We used Mercury, which outputs 1,000+ tokens/sec and is very cheap. The result is a pretty simple combination: Jev for tool selection + Mercury for arguments + WebMCP for the interface. It ends up being very reliable, very fast, and very cheap. A few words about Ultrafast and why do we think it underperforms: Without WebMCP, Jev chooses from the page’s controls: which button to click, which field to fill, or which option to select. But choosing a valid button is different from choosing the right next step. The agent still has to navigate menus, understand forms, recover from errors and recognize when the task is actually complete. Our hypothesis is that WebMCP makes the decision space much simpler. Instead of figuring out a sequence of clicks through a website, Jev chooses explicit actions that directly advance the task. @typesafeai itself documents weaker accuracy on questions requiring multiple reasoning steps. WebMCP moves much of that complexity into the website’s tools, leaving Jev with clearer decisions and fewer opportunities to go wrong (in a sense WebMCP "compresses" a sequence of clicks into one tool call). Our modified Ultrafast setup solved 25/49 tasks - that is a result for our particular implementation and benchmark, not a universal limit on Jev or Browser Use. We are open to more harness optimization to get this result to perform better, feel free to directly contribute to the benchmark here: https://github.com/nekuda-ai/WindTunnel Browser-use ultrafast: https://github.com/browser-use/jev-ultrafast

XAgents and browsers

Jev on the WebMCP benchmark

Tasks solved: 49/49
Model cost: ~112× lower

Kyle Jeong

@kylejeong

we built blazing fast computer/browser use with Jev + @Stagehanddev. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand executes it.

XAgents and browsers

Stagehand on a remote browser

Cost per task: ~$0.001

18K stars in a week

More like this

jev-browser in Cline

Jev on the WebMCP benchmark

Stagehand on a remote browser